Mastering Data Analysis Tools.pptx

7/30/2019 Mastering Data Analysis Tools.pptx

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Data Analysis

Using SPSS

Know where to find information and

how to use it, that's the secret ofsuccess:Albert Einstein


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Workshop Objectives:

To develop Data Analysis Skills

Use of appropriate Statistical Techniques

Use of SPSS to perform Statistical Analysis

Understanding and Interpretation of Results


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Types of Analysis:

Descriptive Analysis Inferential Analysis

Model Building Techniques Multivariate Analysis

IBM SPSS


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Before starting analyzing data let

me introduce SPSS and its basicstructure


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Descriptive Analysis:

Descriptive Analysis for Qualitative Variables

Descriptive Analysis for Quantitative Variables


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Descriptive Analysis of Qualitative Data

Tables Graphs Numbers

One Way TableTwo Way Table

.

.

.

N-Way Table

Bar Chart

Pie Chart

Clustered Bar

Chart

Percentages

Qualitative Data

(Categorical Data)


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Descriptive Analysis for Quantitative Data

Quantitative Data(Numerical Data)

Tables Graphs Numbers

CenterImportant

PointsVariation Distribution

Mean

Median

Mode

Geometric Mean

Harmonic Mean

Trimmed Mean

Median

Quartiles

Percentiles

Range

Inter Quartile Range

Variance

Standard Deviation

Skewness

Kurtosis

Frequency Distribution

Stem and Leaf

Histogram

Box-Plot


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Tabular Methods


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Graphical Methods

0

50

100

150

200

250

15750 35750 55750 75750 95750 115750

Frequency

Histogram


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Numerical Methods


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Practice Session for Descriptive

Analysis

Import Customers Databas.xls into SPSS

Label data properly

Make One-way tables for variables (Age, Sex,OwnHome and Married). Also make pie chart and bar

chart for these variables Make Two-way tables (sex by OwnHome and Married

by OwnHome). Also make clustered bar chart for eachvariable

Produce Detailed Numerical descriptive statistics forvariable Purchases (Mean, Median, ..). Alsomake histogram and stem & leaf and box-plot forvariable Purchases

Perform previous step by gender


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Inferential AnalysisComparing Groups


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Comparing

Groups

OneGroup

MeasuredOnce

NormalityAssumption

Fulfilled

NotFulfilled

MeasuredTwice

NormalityAssumption

Fulfilled

NotFulfilled

TwoGroups

NormalityAssumption

Fulfilled

NotFulfilled

Homogeneity ofVariances

Assumption

Fulfilled

NotFulfilled

More thanTwo

Groups

NormalityAssumption

Fulfilled

NotFulfilled

Homogeneity ofVariances

Assumption

Fulfilled

NotFulfilled

Parametric & Non-Parametric

Inference

Normality

+

Equal

Variances

Normality

+

Un-Equal

Variances

Normality

+

Un-Equal

Variances

Normality

+

Equal

Variances


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Comparing One Group

Kinds of Research Questions

For the one-sample situation, the prime concern in research is

examining a measure of central tendency (location) for the

population of interest. The best-known measures of locationare the mean and median. For a one-sample situation, we

might want to know if the average waiting time in a doctor's

office is greater than one hour, or if the average growth of

roses is 4 inches or more with a certain fertilizer, oris annualreturn is 10.2% for the banks that exercised comprehensive

planning.


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Comparing Two Groups


One of the most common tasks in research is to compare twopopulations (groups). We might want to compare the income levelof two regions, the nitrogen content of two lakes, or theeffectiveness of two drugs.

The first question that arises is what aspects (parameters) of thepopulations shall we compare. We might consider comparing theaverages, the medians, the standard deviations, the distributionalshapes (histogram), or maximum values. We base the comparison

parameter on our particular problem.Perhaps the simplest comparison that we can make is between themeans of the two populations.


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Comparing more than two Groups


One of the most common tasks in research is to compare severalpopulations (groups). We might want to compare the income levelof three regions, the nitrogen content of four lakes, or theeffectiveness of four drugs.

The first question that arises concerns which aspects (parameters)of the populations we should compare. We might considercomparing the means, medians, standard deviations, distributionalshapes (histograms), or maximum values. We base the comparison

of parameter on our particular problem.Perhaps the simplest comparison that we can make is to comparemeans of several populations.


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One Sample t-test

One Sample t-test is used to compare one group to a

given standard on the basis of Arithmetic Average

(Mean).


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Assumptions of the One-sample t-test

The data are continuous.

The data follow the Normal distribution.

The sample is a simple random sample from the

population.


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Hypotheses and Formulas

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Case Study

A manufacturer of high-performance automobiles

produces disc brakes that must measure 322 millimeters

in diameter. Quality control manager randomly selects

128 discs and measures their diameters.

We can use One Sample T Test to determine whether or

not the mean diameters of the brakes in sample

significantly differ from 322 millimeters.


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SPSS Analytic Procedure


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The Sign Test

The sign test is perhaps the oldest of all the nonparametricprocedures. This nonparametric test is based on thebinomial distribution. It assumes two mutually exclusiveoutcomes, constant or stable probability of success orfailure, and n independent trials

The terminology, sign test, reinforces the point that thedata are converted to a series of plus and minus signs. Thetest is based on the number of plus signs that occur. Zerodifferences are thrown out, and the sample size is reduced

accordingly.


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Assumptions of the Sign Test

The data are continuous

The distribution of these data is symmetric.

The measurement scale is at least interval.


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Case Study

A Researcher believes that median salary of HRManager is 50 thousands. To confirm thishypothesis he selects a random sample of 1207

HR Managers from different companies.

We can use Sign Test to determine whether ornot the median salary is significantly different

from 50 thousands.


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Paired Samples t-test


In the paired case, we take two measurements on sameindividual at different times, or we have one measurement

on each individual of a pair.Examples of the first case are two insurance-claim adjustersassessing the damage for the same 15 cases. Evaluation ofthe improvement in aerobic fitness for 15 subjects wheremeasurements are made at the beginning of the fitnessprogram and at the end of it.An example of the second paired situation is the testing ofthe effectiveness of two drugs, A and B, on 20 pairs ofpatients who have been matched on physiological andpsychological variables. One patient in the pair receivesdrug A, and the other patient gets drug B.


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Assumptions of the paired-sample t-test

The data are continuous.

The data, i.e., the differences for the matched-pairs,

follow a Normal distribution.

The sample of pairs is a simple random sample from

its population.


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Case Study

A researcher in behavioral medicine believes that stress oftenmakes asthma symptoms worse for people who suffer from thisrespiratory disorder. Therefore, the researcher decides to study theeffect of relaxation training on the severity of their symptoms.

A sample of 5 patients is selected. During the week beforetreatment, the investigator records the severity of their symptomsby measuring how many doses of medication are needed forasthma attacks. Then the patients receive relaxation training. Forthe week following the training the researcher once again recordsthe number of doses used by each patient.

Data from Gravetter and Wallnau (4th Ed.) p. 319.


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Wilcoxon Signed Rank test

Wilcoxon Signed Rank test is used to test the

median difference of zero in case ofnon

normal populations.


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Assumptions of the two-sample t-test

The differences are continuous.

The distribution of these differences is symmetric.

The differences are mutually independent.

The measurement scale is at least interval.


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Case Study

An educationist wants to see the effectiveness of

new teaching method. For this She selected 600

students and record their scores in a test of 150

marks. The scores are recorded before and after thenew teaching method.

The Wilcoxon Signed Rank test can be used to test

the effectiveness of new teaching method.


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Independent Samples t-test

Equal Variances

Independent sample t test is used to compare two

groups on the basis of their averages.


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Assumptions of the two-sample t-test


The data follow the Normal distribution.

The variances of the two populations are equal

The two samples are independent

Both samples are simple random samples from their

respective populations.


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Case Study

An analyst at a department store wants to evaluate a

recent credit card promotion. To this end, 500

cardholders were randomly selected. Half received

an ad promoting a reduced interest rate onpurchases made over the next three months, and

half received a standard seasonal ad.

We can use Independent-Samples T Test to compare

the spending of the two groups.


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Independent Samples t-test

Unequal Variances

Independent Samples t-test is use to compare two

independent groups on the basis of average. This test

does not require homogeneity of the variances.


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Case Study

A researcher wishes to compare the

expenditure behavior of the students, one of

the research question is to see the difference

in expenditures by gender.


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Mann-Whitney Test

Mann-Whitney Test is used to compare the

two independent groups on the basis of

medians. This test does not require the

assumption of normality.


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Mann-Whitney U Test Assumptions

The variable of interest is continuous. The measurement scale

is at least ordinal.

The probability distributions of the two populations are

identical, except for location.

The two samples are independent.

Both samples are simple random samples from their

respective populations.


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Case Study

Data on birth weight of infants born to mothers with

different levels of prenatal care. Two independent

samples data for univariate analysis. Test data for

Mann-Whitney U-Test, obtained from Howell, DavidD. Fundamental Statistics for the Behavioral Sciences

3rd Edition, p385.


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O W A l i f V i


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One-Way Analysis of Variance

Equal Variances

One Way Analysis of Variance is used to

compare more than two groups on the basis

of their averages.

O W A l i f V i


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Assumptions The data are continuous.

The data follow the Normal distribution, each groupis normally distributed.

The variances of the populations are equal.

The groups are independent.

Each group is a simple random sample from itspopulation.


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MSGF

MSE

MSG is the Mean Square of Group and MSE is the Mean Square Error


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Example

This is a hypothetical data file that concerns the

popularity of a TV channel. Using a prototype, the

marketing team has collected focus group data. One

of the question of interest is to see the difference inpopularity of the TV channel in different age groups.

This hypothesis can be tested using One Way ANOVA.


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One Way Analysis of Variance


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Unequal Variances

Welch ANOVA is used to compare more than two

groups on the basis of averages. This test does not

require the homogeneity of variances.

Welch Analysis of Variance


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Welch Analysis of Variance

Assumptions


The data follow the Normal distribution, each group is

normally distributed.


Each group is a simple random sample from its population.


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Case Study

A sales manager evaluates two new training courses.

Sixty employees, divided into three groups, all

receive standard training. In addition, group 2

receives technical training, and group 3 receives ahands-on tutorial. Each employee was tested at the

end of the training course and their score recorded.


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Kruskal-Wallis Test

Kruskal-Wallis H-test is used to compare more

than two groups on the basis of their medians.

Kruskal Wallis Test


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Kruskal-Wallis Test

Assumptions

The variable of interest is continuous, themeasurement scale is at least ordinal.

The probability distributions of the populations areidentical, except for location.


All groups are simple random samples from theirrespective populations.


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Case Study

A health scientist wishes to compare the

survival experiences after breast cancer with

different Pathological Tumor Size (Categories).

We can use Kruskal-Wallis H-Test to determine

whether or not the median survival time of

the patients is significantly differ in differentpathological tumor size.


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Model Building Techniques

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